Research reportUsing crowdsourcing to compare temporal, social temporal, and probability discounting among obese and non-obese individuals☆
Introduction
Obesity is a major public health problem that impacts approximately 36% of American adults (CDC, 2013). Several conceptualizations of obesity suggest that the failure of self-control, sometimes called impulsivity or executive dysfunction, plays a major role in the genesis and maintenance of weight control problems (Epstein, Salvy, Carr, Dearing, & Bickel, 2010). Specifically, the immediate impulse to consume food prevails over the future health goals, which can be obtained by engaging in fewer and smaller bouts of eating. From behavioral and neuroeconomic perspectives, this failure of self-control results from normal learning mechanisms bring commandeered by the pathological processing of food rewards, further this dysfunction becomes exacerbated over time (Bickel et al., 2007). These pathological valuations may distort decision making by altering the regulatory balance between two neurobehavioral decision systems, resulting in an over-valuation of immediate commodities (e.g., food), and an under valuation of longer-term commodities (e.g., improved health) (Bickel et al., 2012, Bickel et al., 2011, Carr et al., 2011). Importantly, delay discounting, which measures the decline in the value of a reinforcer as a function of the delay to its receipt, reflects the regulatory balance between impulsive (i.e., limbic and paralimbic areas) and executive (prefrontal cortex) neurobehavioral systems (Bickel et al., 2007). Individuals suffering from a variety of behavioral disorders (e.g., drug addiction, pathological gambling, etc.) excessively discount delayed rewards, suggesting that excessive discounting may function as a trans-disease process (Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012).
Delay discounting has been shown to be an index of food reinforcement (Epstein et al., 2010), and this is true on both the physiological and behavioral level, however the few studies that have used delay discounting to research the eating behavior of obese subjects have produced inconclusive results. Physiologically, discounting in those who engage in excessive hedonic eating is thought to be a manifestation of systemic dopaminergic dysfunction (see Appelhans, 2009, for a review). Further, activity in regions of the prefrontal cortex that were more active on difficult delayed discounting trials were predictive of weight gain (within the next two years) in obese subjects (Kishinevsky et al., 2012), however a follow-up study on this same group of subjects failed find a correlation between current BMI measures and discounting (Stoeckel, Murdaugh, Cox, Cook, & Weller, 2013). Behaviorally, discounting has been shown to be an index of excessive eating and resulting poor health across a number of different dimensions. Obese women, but not men, were shown to discount more than normal weight women, normal weight men, and obese men (Weller, Cook, Avsar, & Cox, 2008). Likewise, obese adolescent smokers have been found to discount more than normal-weight smokers (Fields, Sabet, Peal, & Reynolds, 2011). Similarly, normal-weight adolescents discount less than overweight and obese adolescents, and overweight adolescents discount less than obese adolescents (Fields, Sabet, & Reynolds, 2013). Obese and overweight women with greater discounting order more high-energy foods, and consumed more calories, when eating away-from home meals, but this is not true of meals prepared at home (Appelhans et al., 2012). Finally, both male and female subjects with higher percent body fat have greater discounting for food rewards (Hendrickson and Rasmussen, 2013, Rasmussen et al., 2010), but only female participants with higher percent body fat show greater discounting for monetary rewards (Rasmussen et al., 2010).
The study of discounting in the obese so far has not examined a relatively new variant of the delayed discounting procedure referred to as social temporal discounting (Bickel et al., 2012, Bickel et al., 2012, Bickel et al., 2012, Charlton et al., 2013). Social temporal discounting provides a measure of the decline in value of a reinforcer that will be distributed to a group of individuals (including the participant) as a function of the delay to the reinforcer’s receipt (Bickel, Jarmolowicz, Mueller, Franck, et al., 2012). This procedure presents participants with choices between a reinforcer that will be evenly distributed among a group of unspecified individuals (including the participant) now vs. a larger reward to be shared among that same group later (i.e., “we now” vs. “we later”; Charlton et al., 2013). Comparison of standard delay discounting (i.e., “me now” vs. “me later”) and social temporal discounting among college students resulted in greater preference for the delayed option (less discounting) in the social temporal option relative to preference in the standard discounting procedure. Moreover, a study examining temporal discounting, social temporal discounting, and a combination of standard temporal discounting and social temporal discounting (i.e., “me now” vs. “we later”) in problem drinkers showed comparable results to controls on the extent of discounting on social temporal and the combination discounting task, while smokers discounted significantly more on all forms of discounting relative to controls (Bickel et al., 2012, Bickel et al., 2012, Bickel et al., 2012, Bickel et al., 2012). Thus, whether social temporal discounting engenders more or less preference for the delayed option is, in part, dependent on the population under study.
In this study, we obtained a large sample of participants (n = 1181) by using Amazon’s Mechanical Turk (AMT). AMT, a crowdsourcing service, permits researchers to post tasks or questions which are then answered by a potential participant pool of more than 500,000 potential research volunteers to complete (The Economist, 2012). This participant pool is considerably more diverse than typical university-based samples (Buhrmester et al., 2011, Jarmolowicz et al., 2012, The Economist, 2012), and studies using AMT as a source of participants have replicated previously reported findings (Bickel et al., 2012, Sprouse, 2011). The present study used AMT to collect data from a large sample to clarify the extent of differences in temporal, social temporal discounting, combination of standard and social temporal discounting, and probability discounting between obese and control participants.
Section snippets
Participants
Individuals (N = 1181) from across the United States of America took a 198-item questionnaire about health behaviors, sociality, and monetary decision-making through the AMT crowdsourcing service. To access the survey, individuals had to be registered with AMT, be at least 18 years old, and successfully complete at least 90% of their previous Human Intelligence Tests (HITs). Before participating, participants were provided with an overview of the study. Implied consent was obtained from
Results
A preliminary analysis was conducted to compare normal (BMI <25), overweight (25⩽ BMI <30), and obese (BMI ⩾30) individuals in terms of ln(k) and ln(h) for the four discounting measures. Discounting was modeled as a function of BMI group and model contrasts were used to compare each of these three groups. Non-obese individuals (n = 596) and overweight individuals (n = 304) were not significantly different for any of the four discounting measures (p values ranged from 0.1762 to 0.7083). Therefore these
Discussion
In this study, crowdsourcing technology (i.e., AMT) was used to obtain discounting data from a large sample of obese and non-obese participants. Overall, we found that the obese discounted more than controls in temporal discounting and social temporal discounting, but not probability discounting. We also found that the best model to predict obesity as identified by BIC included age, and ln(k) from the standard (me now vs. me later) discounting procedure. This observation contributes to the
Conclusions
In conclusion, this report provides evidence that temporal discounting and social temporal discounting are different when comparing those with obesity to controls, but that the effect size is small. As such, this study suggests a reason for the inconsistent findings observed in prior studies. Future research might consider decomposing the obese sample into different subgroups and perhaps identifying different phenotypes as indicated above (e.g., reinforcer pathology). Nonetheless, these
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Acknowledgements: This work was funded by NIH Grants R01 DA 024080, R01 DA 024080-02S1, R01 DA 030241, R01 DA 034755, R01 AA 021529, and the Virginia Tech Carilion Research Institute. The authors would like to thank Patsy Marshall for assistance with manuscript preparation.